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AG Mathematics of Deep Learning /KursID:878
- Letzter Beitrag vom 2021-07-26
Schlüsselworte:
functional
minimization
methods
framework
approximation
control
distance
reconstruction
energy
deep
search
basic
weights
models
measure
layer
activation
problem
example
propagation
Lehrende(r)
Dr. Daniel Tenbrinck
Einrichtung
Lehrstuhl für Angewandte Mathematik
Aufzeichnungsart
Vortragsreihe
Zugang
Frei / IdM-Anmeldung
Sprache
Zugehörige Einzelbeiträge
Folge
Titel
Lehrende(r)
Aktualisiert
Zugang
Dauer
Medien
Search course clips:
1
Introduction to Deep Learning
Dr.-Ing. Vincent Christlein
2019-11-14
Frei
01:22:04
Folien
Audio
captions
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2
Mathematics - Deep Learning
Prof. Dr. Martin Burger
2019-12-05
Frei
01:35:25
Folien
Folien
Audio
captions
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3
Known Operator Learning - Towards Integration of Prior Knowledge into Machine Learning
Prof. Dr. Andreas Maier
2020-01-09
Frei
00:44:56
Folien
Folien
Audio
captions
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4
Biomechanics meets Optimal Control: Predictions of Human Motion
Prof. Dr. Anne Koelewijn
2020-02-13
Frei
01:27:21
Folien
Folien
Audio
captions
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5
A New Approach for Stochastic Optimization in Deep Learning
Dr. Lukas Pflug
2020-02-20
Frei
00:31:51
Folien
Folien
Audio
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6
Introduction to Adversarial Robustness for Deep Learning
Dr. Leo Schwinn
2020-11-11
Frei
00:53:41
Folien
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7
The interplay of deep learning and control theory
2020-12-09
Frei
01:04:25
Folien
captions
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8
Generalised Perceptron Learning
2020-12-16
Frei
01:10:53
Folien
captions
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9
Neuromorphic Computing and its Mathematical Challenges
2021-01-13
Frei
01:17:23
Folien
captions
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10
Structure Preserving Deep Learning
2021-01-27
Frei
01:07:37
Folien
captions
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11
Mathematical aspects of neural network approximation and learning
2021-04-20
Frei
01:02:09
Folien
captions
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12
Deep Learning meets Shearlets: On the Path Towards Interpretable Imaging
2021-04-27
Frei
00:50:44
Folien
captions
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13
On the Confluence of Deep Learning and Energy Minimization Methods for Inverse Problems
2021-05-04
IdM-Anmeldung
00:53:30
Folien
captions
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14
Neural Differential Equations, Control and Machine Learning
2021-05-11
Frei
00:57:53
Folien
captions
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15
An Introduction to Generative Modeling
2021-05-18
Frei
00:55:59
Folien
captions
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16
Random walks and PDEs in graph-based learning
2021-05-25
Frei
01:00:32
Folien
captions
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17
Learning with energy-based models
2021-06-01
Frei
01:04:59
Folien
captions
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18
Learning of Wasserstein Generative Models and Patch-based Texture Synthesis
2021-06-08
Frei
01:01:40
Folien
captions
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19
Self-supervised Learning for 3D Shape Analysis
2021-06-18
Frei
00:34:20
Folien
captions
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20
Deep Learning for Computed Tomography Image Reconstruction from Insufficient Data
Dr. Yixing Huang
2021-06-26
Frei
00:41:12
Folien
captions
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21
Deeply learned regularisation for inverse problems
2021-06-29
Frei
00:58:36
Folien
captions
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22
Online Learning for Optimization Problems with Unknown or Uncertain Cost Functions
2021-07-06
Frei
00:29:47
Folien
captions
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23
Biomechanics meets Deep Learning
Prof. Dr. Anne Koelewijn
2021-07-13
Frei
01:10:13
Folien
captions
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24
Accelerated Forward-Backward Optimization using Deep Learning
2021-07-26
Frei
00:46:52
Folien
captions
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Mehr Kurse von Dr. Daniel Tenbrinck
Orientierungswoche Data Science 2021/2022
Dr. Daniel Tenbrinck
Vorlesung
2021-10-14
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Discrete Graph Operators for Manifold-Valued Data
Dr. Daniel Tenbrinck
2020-11-04
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Mathematical Data Science 1 2021
Dr. Daniel Tenbrinck
Vorlesung
2021-06-17
Frei
Mathematik für Data Science 2 / Physikstudierende B 2021
Dr. Daniel Tenbrinck
Vorlesung
2021-07-16
Frei
Mathematik für Physikstudierende C 2021/2022
Dr. Daniel Tenbrinck
Vorlesung
2022-02-17
Frei
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